- Architect, build, and optimize distributed systems for real-time path planning in multi-agent environments using Machine Learning and Reinforcement Learning
- Own the end-to-end ML lifecycle, including data ingestion, feature engineering, model training, validation, deployment, and monitoring
- Lead production deployment and lifecycle management of ML/RL models using MLOps platforms such as Vertex AI or equivalent
- Integrate ML pipelines with robotic orchestration and control systems to enable continuous learning and adaptive behavior
- Serve as a technical mentor and reviewer for ML engineers; define and uphold best practices for code quality, model performance, and system reliability
- Collaborate cross-functionally with software, robotics, product, and operations teams to deliver scalable ML solutions for real-world fulfillment challenges
- Debug, profile, and optimize ML systems for latency, throughput, and reliability in real-time and distributed environments
- B.E. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related field
- 8+ years of total professional experience, with 6+ years in AI / Machine Learning systems
- Proven success as a Senior or Principal Individual Contributor delivering production-grade ML systems
- Experience influencing architecture and technical direction without direct people-management responsibility
- Strong expertise in path planning, graph search algorithms, optimization techniques, and multi-agent systems
- Deep experience of Machine Learning, Deep Learning, and Reinforcement Learning
- Hands-on experience with TensorFlow or PyTorch
- Demonstrated success in building, deploying, and maintaining ML models at production scale
- Strong MLOps experience, including:
- Model CI/CD pipelines
- Workflow and pipeline orchestration
- Model monitoring, drift detection, and retraining strategies
- Advanced proficiency in Python
- Solid understanding of distributed systems, concurrency, parallelism, and real-time processing
- Strong computer science fundamentals: algorithms, operating systems, networking, memory management, and performance tuning
- Experience with microservices, Docker, Kubernetes, and containerized workloads
- Experience with event-driven architectures and asynchronous processing
- Hands-on experience with cloud platforms, preferably Google Cloud Platform (GCP) and Vertex AI
- Familiarity with Erlang, Elixir, or other concurrency-first functional languages
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Principal Engineer – AI - San Francisco - FlairX
Description
Overview
Compensation: Open / Competitive + Full Benefits
Principal Engineer – AI / Machine Learning
Location: Hybrid (Redwood City, CA)
Employment Type: Full-Time
Interview Mode: In-Person (Redwood City, CA)
About the Role
We are seeking a Principal Engineer – AI/ML to lead the design and delivery of high-performance, real-time machine learning systems powering multi-agent path planning and intelligent robotic decision-making.
This is a hands-on principal engineer role, focused on architecture, algorithms, and production-grade ML systems at scale. You will work closely with robotics, platform, and product teams to translate complex real-world problems into reliable, low-latency AI solutions.
Key Responsibilities
Required Qualifications
Education
Experience
Technical Expertise
Nice to Have
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Principal Engineer
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